---
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: whisper-base-malayalam-colab-CV17.0
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_17_0
      type: common_voice_17_0
      config: ml
      split: test
      args: ml
    metrics:
    - name: Wer
      type: wer
      value: 0.7675693101225016
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# whisper-base-malayalam-colab-CV17.0

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4369
- Wer: 0.7676

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.15
- training_steps: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 1.0335        | 1.5748  | 200  | 0.4105          | 0.9504 |
| 0.2301        | 3.1496  | 400  | 0.3121          | 0.8417 |
| 0.0954        | 4.7244  | 600  | 0.2964          | 0.8288 |
| 0.0442        | 6.2992  | 800  | 0.3350          | 0.7843 |
| 0.0217        | 7.8740  | 1000 | 0.3740          | 0.8133 |
| 0.0104        | 9.4488  | 1200 | 0.3858          | 0.7782 |
| 0.0048        | 11.0236 | 1400 | 0.4128          | 0.7747 |
| 0.002         | 12.5984 | 1600 | 0.4319          | 0.7747 |
| 0.0006        | 14.1732 | 1800 | 0.4324          | 0.7701 |
| 0.0002        | 15.7480 | 2000 | 0.4369          | 0.7676 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1